This section provides background information related to the present disclosure which is not necessarily prior art.
Neural networks represent a class of artificial intelligence whereby programmed computers perform pattern recognition, make predictions and/or provide response to input stimulus. While fundamentally machine in nature, neural networks have properties that mimic biological systems or are inspired by biological systems such as the human brain. Neural networks are useful in a wide variety of applications where input stimuli are used to train the network, so that over time, the network is able to “recognize” patterns it has previously encountered.
While some neural networks can be custom-configured to match the needs of a particular problem, the present disclosure is concerned with developmental networks that are not architecturally configured by the network designer to match the intended purpose. Rather, the developmental network is more akin to un-molded clay that becomes adapted to its special purpose through the learning process. The network designer has no direct access to that clay, but instead must rely on the network to configure itself through training. The developmental network is sometimes referred to as a “skull-closed” network, to convey the sense that the network designer does not have direct access to the neurons, just as a human teacher has no direct access to the neurons inside the student's brain.
To train a developmental network by brute force can take a very long time. One can liken the process to the way the human genome evolved over many thousands of years. The DNA molecule that defines each of us today is the result of millions upon millions of copy and clone operations that occasionally mutated to discover a “better” configuration, which then succeeded where the earlier configurations failed. Thus eventually our genome has “learned” how to make the proteins that define our bodies.
Interesting as the human genome analogy may be, it is clearly not practical to design an artificial intelligence system that must take many thousands of years to learn a desired behavior. Thus techniques are needed to speed up the process.